Embodiments of the invention relate generally to automated angiogram analysis, and more specifically, to finding similar coronary angiograms in a database.
Cardiovascular disease (CVD) results in more death and disability in both males and females in all western societies than any other disease category, accounting for one third of all deaths in the United States in 2007. Half of these deaths are due to coronary artery disease (CAD), which is the process of atherosclerotic narrowing of coronary arteries which are arteries that supply blood and oxygen to the heart muscle. Any occlusion of these arteries can impact heart function leading to effort intolerance with exertion provoked chest symptoms, heart attack, permanent impairment of heart muscle function, and sudden death.
The “gold standard” diagnostic test for CAD is the coronary angiogram. A coronary angiogram involves placing plastic catheters into the arterial system and injecting iodinated contrast solution into the coronary blood flow, to obtain a silhouette of the coronary arterial wall. The angiographic images are typically recorded at 15 or 30 frames a second, providing a motion picture of the flowing blood and contrast mixture to permit the identification of segmental coronary narrowing or blockage attributable to atherosclerotic plaque accumulation along the interior wall of the artery.
Interpretation of coronary angiograms is nearly always performed by visual estimation of the severity of narrowing in the diseased coronary artery, stated in percent of diameter lost in projections that display the narrowing at its worst. Because of the curvilinear cylindrical structure of an artery and the irregular and often eccentric remaining lumen through the diseased segment, several viewpoints are inspected in the effort to estimate the percentage of narrowing.
It is well-known that clinicians look for characteristic visual features during assessment taking into account the overall disease burden, the complexity of individual lesions (bifurcation or not),and placing more weight on proximal stenoses of the coronary arteries. Such semantic features often resolve to visual attributes such as lumen variation across an artery indicating non-homogenous opacification caused by the mixing up of dye and calcium deposits. Even though there are quantitative assessment scores such as the syntax score, they require manual input of angiographic information. In many cases, clinicians still characterize the disease by ‘eyeballing’ on salient visual features such as the relative thickness of arteries, the distance of the junctions from the root, the number of trifurcations, etc.